CN116324656A - Monitoring of a converter - Google Patents
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- CN116324656A CN116324656A CN202180070802.8A CN202180070802A CN116324656A CN 116324656 A CN116324656 A CN 116324656A CN 202180070802 A CN202180070802 A CN 202180070802A CN 116324656 A CN116324656 A CN 116324656A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/40—Testing power supplies
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
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Abstract
In a method for monitoring a power converter (1), a plurality of warning messages (2) are used in order to determine an error condition of the power converter (1). Wherein the current transformer has at least two types of warning messages, a first warning message type having a warning message which is dependent on the type of the current transformer, and a second warning message type having a warning message which can be defined by a user, wherein a combined evaluation of the respective first type and the respective second type of warning is used for monitoring, wherein the different types of warning messages (2) are grouped, wherein the grouping of warning messages (2) corresponds to an element of the current transformer (1).
Description
Technical Field
The invention relates to the monitoring of a power converter or of a drive, wherein the drive has at least one power converter, wherein the drive can also have an electrical machine, such as an electric motor or a generator, in addition to the power converter.
Background
The converter enables a variable-speed operation of the electric machine (for example, an electric motor). When the electric machine is used as a generator, a current transformer can also be used to convert the current. In this way, for example, a grid feed can be realized. For example, for motor applications, grid power having a constant frequency and voltage is converted to power having a variable frequency and voltage.
The converter is, for example, an inverter, a rectifier or a power converter. The converter can be water-cooled and/or air-cooled. For example, current transformers are used in applications where reliability and quality are a high requirement. Examples of applications of the current transformer include:
industrial pump and fan
Oil air pumps and compressors, e.g. electric submersible pumps and high-speed compressors
Boiler fan for generating electricity (induced draft and forced draft)
Clean water pump and waste water pump
Multiple motor applications and synchronous transmissions (e.g. pipelines in the oil and gas industry),
etc.
These application examples generally relate to the use of converters, where high power is particularly required. This is in particular in the megawatt range of one-, two-or three-digit power. For this purpose, a converter involving medium voltage is preferably used. These can be described as medium voltage converters. Voltages greater than or equal to 1000V may be considered medium voltage. Voltages of 4000V or 6000V may also be referred to as medium voltages. In addition to the power semiconductors, the converter has a regulator or a controller, for example a transformer and/or a choke. The converter is for example a Variable Frequency Drive (VFD).
If the current transformer has problems, the problems can be identified and recorded or saved in a log file. The log file represents a log in which the identified and recorded problem is a log entry. These log entries can be warning the user of potential critical events. These log entries can also be errors, i.e., error messages, in order to inform the user of the error or to record the error. For example, critical events are also errors. An error message or warning message may be generated for the event. For example, the error message can also be an interference report. Thus, in the presence of errors, in particular in the presence of disturbances. Errors can cause or have caused the converter and/or the drive to fail. For example, if a drive has a problem, the identified problem may be recorded in a log entry. These log entries can be alerts (to alert the user to potential critical events) or errors (to record errors that lead to drive failure).
An alert indicating a suboptimal state of the drive is currently sent from the drive or the current transformer to the UI (user interface), e.g., accessible via the HMI of the drive or the current transformer and/or via a cloud solution. The one or more alerts are then analyzed by the respective driving specialist and actions taken and implemented by the specialist.
Disclosure of Invention
It is an object of the invention to improve the monitoring of a converter or a drive.
The solution of this object is given by the method according to claim 1 or by the analysis system according to claim 11. For example, some embodiments are given in accordance with claims 2 to 10.
In a method for monitoring a converter, a plurality of warning messages are used in order to derive an error situation of the converter. Here and below, the method involves, in addition to the current transformer, a drive having a current transformer. In addition to the current transformer, the drive can also have a transmission and/or an electric motor. The inference of error conditions also relates to the prediction of error conditions. Thus, prediction of an error condition relates to a situation where an error has not yet occurred but will occur with some probability after prediction. In this case, errors also relate in particular to disturbances in the converter or the drive.
Such a method for monitoring of the converter uses, for example, log data from the converter.
In a method for monitoring a power converter, a plurality of warning messages are used to infer an error situation of the power converter, wherein the power converter has at least two types of warning messages, a first warning message type having warning messages that depend on the type of the power converter, and a second warning message type having warning messages that can be defined by a user, wherein for monitoring the power converter a combined evaluation of a respective first type and a respective second type of warning is used, wherein the different types of warning messages are grouped, wherein the grouping of warning messages corresponds to elements of the power converter. The current transformer can therefore have at least two types of warnings, a first warning type which is dependent on the type of current transformer, i.e. can be determined in particular by this type, and a second warning type, wherein the second warning type has a warning which can be defined by the user, i.e. is determined by the user, wherein for event monitoring a combined evaluation of the respective first type and the respective second type of error or warning can also be used. The converter can thus be configured, for example, by a user, such that the user defines individual messages for the converter. Examples of reports are errors or warnings, i.e. error messages or warning messages. By personalizing the message, personalized event monitoring can be created, which relies on the user personalizing the created message. This improves event monitoring and enables more accurate. The messages defined by the user are stored, for example, in a SOP (system operating program) or defined there. Such user-defined messages can be marked when the report is displayed. For example, the user-defined message is based on signals from an I/O interface present in the facility integrating the converter. For example, the signal on which the user defined report is based can be a digital signal or an analog signal. For example, the user-defined message can be generated based on a single signal and/or based on a combination of signals. For example, such signals can relate to emergency stops, opening of doors, fuse blowing, undervoltage, overvoltage, false current, overcurrent, fan failure, power module failure of the converter, bypass of the power module of the converter, insulation errors, insulation warnings, communication errors, in particular communication errors of the power module of the converter, errors or warnings of cooling of the converter, pre-charging of the converter, etc. It can be seen that messages created separately for converters involving separate use (industrial facilities) are important. These messages created by the user alone can be advantageously used for event monitoring of the converter in its individual environment, i.e. the industrial installation. This improves the quality of the monitoring. The user of the power converter is, for example, a person who operates the power converter. This operation can be performed, for example, by an operator or a commissioning person of the converter.
In one embodiment of the method, the warning message, i.e. the warning, can for example relate to the following subjects: the temperature of the cooling body is too low, the temperature of the cooling body is too high, the current is asymmetric, the temperature in the converter is too high, the temperature of the input reactor almost reaches the turn-off limit, the cabinet heating fault, the motor winding temperature is too high, the insulation fault, the cabinet fan fault, the low rotation speed, the motor locking and/or the control voltage and the like. Such warning messages can be created individually for the converter, facility-specific. Depending on the installation, different requirements can be placed on the converter. The cooling of the converter can also be facility-specific, so that individual warnings are created for the converter in a specific facility environment and then combined into groups. There can also be at least one warning message in such a group, which warning message depends on the type of converter. Thus, a separate prediction of the feasible errors is possible, which are facility specific.
In one embodiment of the method, the second type of warning message is dependent on the installation in which the converter is integrated. Such a dependency can for example relate to at least one of the following: external cooling of the converter, external temperature sensor, external humidity sensor, external safety sensor (e.g. door switch), external voltage monitoring of the power supply of the controller, sensor, etc
In one embodiment of the method, the second type of warning message is dependent on a combination of signals. The combination can be selected by a user. The combination relates in particular to boolean connections, such as AND, OR, NOT. Examples of signals relate in particular to the following reports: voltage supply to the power electronics, voltage supply to the control and/or regulation electronics, door opening, door closing, etc. Thus, the user can design a separate warning.
In one embodiment of the method, a fault of the converter can be predicted by an algorithmic analysis of the warning (warning message). The warning contains not only quantitatively measurable information (e.g. warning by threshold value) but also qualitative information about the state of the converter or the drive. For this purpose, algorithms can be used.
In one embodiment of the method, the warning messages are grouped, wherein the grouping of the warning messages corresponds to the elements of the converter. Thus, for example, the following warning groups can be provided: transformers, cooling, bypass, power semiconductors, batteries, electrical machines, transmissions, and/or bearings. These groups can be linked to each other in terms of analysis of the warning message. Alert messages from different groups can be interdependent.
In one embodiment of the method, the warning message can be retrieved, for example, from log data. In one embodiment, for example, a specific time period and/or a time period after the converter restart can be taken into account when the warning is taken into account. In this way, it is for example possible to use or consider only relevant warning messages that are relevant in terms of time.
In one embodiment of the method, one or more warning messages are identified, i.e. marked, with an identification, i.e. a marker, which relates to a time sequence, for example. For example, the time series is generated by a time stamp linked with the warning message (and vice versa). However, the identification, i.e. the marking, of the warning message can also be, for example, the degree of urgency of the warning message, the source of the report and/or the name (description) of the warning message, etc.
In one embodiment of the method, the error prediction involves a prediction of an error situation. Erroneous inference means that if no countermeasures are taken, an error condition is expected from one or more warning messages (in combination or alone). In an advantageous embodiment, the prediction of the error situation is also associated with a time specification. The time specification gives, for example, a reply about when an error can be calculated with a certain probability from a specific point in time (e.g. the withdrawal of a warning message or the invocation of a warning message). Thus, it can be derived, for example, that as the waiting time increases, the probability of occurrence of an error increases by a specific value.
In one embodiment of the method, at least one rule is used for monitoring, wherein the rule relates to the occurrence of one or more alarms in one or more groups. For example, the occurrence of one or more specific errors in one or more groups can be correlated and analyzed.
In one embodiment of the method, a message for the impending error situation (i.e. in particular for the impending disturbance) is generated by an analysis of the warning message. Therefore, a user of the inverter or the drive can take countermeasures. In one embodiment of the method, this type of countermeasure is determined by analysis and is initiated, in particular, automatically.
In one embodiment of the method, the severity of the impending error is determined. Then, appropriate measures can be taken more in accordance with the severity. This is for example a planned quick repair or possibly presumably time-consuming ordering of spare parts.
In one embodiment of the method, a large number of warning messages can be converted into messages for an impending error situation. In addition to predicting an impending error condition in time, this has the advantage that, for example, the number of messages associated with the user can be reduced. This improves in particular the overview and simple use of the converter or drive. Messages of impending error conditions that can be preferably avoided can be abbreviated as, for example, precog. For example, this concept can be understood as "pre-recognition".
In one design of the method, artificial intelligence is used. According to this method, artificial intelligence can also be trained. Thus, the expert's knowledge can be replaced by artificial intelligence.
In one embodiment of the method, a message is generated that a time-wise occurrence of an error situation has occurred. For example, it is also possible to analyze which further consequences may occur from the occurrence of an error. Thus, it can be advantageous to analyze, for example, whether a rapid or slow response is made to a warning message or a message about an impending error (Precog). This can depend, for example, on the expected downtime.
It is advantageous to react quickly or slowly. 8. The method according to any of claims 1 to 7, wherein a message is generated regarding protection against error conditions.
In one embodiment of the method, the warning message is received via the internet. Thus, for example, the analysis device can receive reports from the converter or the drive via the internet. In particular, after analysis, the analysis means can send data related to the analysis result to a user interface (UI: user interface). Such data can be provided for a user interface via an internet connection, for example. For example, the user interface is located at the location of the converter or drive, the console, etc.
In addition to the method, the invention also relates to an analysis device or an analysis system having an analysis device. The analysis device or analysis system is specifically designed to perform the method. For example, an analysis system can also be referred to as a pre-error system because it can predict potential errors before they occur.
The analysis system of the converter or of the drive has in particular artificial intelligence, wherein the analysis system is in particular spatially separated from the converter.
In one embodiment of the method or of the analysis system, a digital platform for optimizing the drive system, the motor, the converter, etc., is present, wherein reports or log files are collected and/or transmitted. Thus, data can be transmitted from the converter to the analysis system via the internet (i.e. via the cloud). Such data, e.g. logs (log data), are obtained from the digital platform and contain, for example, at least one of the following information:
the time stamp of the log event,
severity of log event: information, an error or a warning is provided,
the text of the log event is presented,
participants of log events (generator/source of log events).
For example, the occurrence of one or more errors after a certain error-free time may indicate a drive failure, which results in an unexpected shutdown of the drive. Errors are in particular alarms with a severity of "error". For example, a drive failure may occur when the drive triggers a shutdown due to one or more activation errors. Some errors, and thus some faults, can be predicted by specific warnings that occur before the error occurs.
The analysis system is specifically designed as a pre-error system and uses developed algorithms to analyze the generated alerts. Thus, the system will identify potentially occurring errors before they actually occur. In particular, if a potential failure of the drive can occur, the analysis system may consider the severity of the alarm to predict the occurrence of errors.
In one design of the system or method, alerts are categorized into consecutive groups and/or expert knowledge based specific decision rules are applied to the groups. For example, four pre-error groups can be created for alerts:
an input/transformer which is connected to the input/transformer,
battery, control, output,
-cooling
-bypass
These groups are mentioned here as examples. The group specifically covers critical components of the drive (in particular all critical components) which can be predicted to fail for a period of at least more than two hours. When a specific warning occurs for a specific group with specific rules, the system will output a positive prediction of a potential drive failure from the identified group associated with the potential critical components of the drive.
In one embodiment of the system, the groups are based on physical relationships of alarms, wherein the alarms can be of different types, and in particular also on historical or statistical evaluation of the operating history of the respective converter type. Thus, the warning of a packet is associated with one type of error. For example, physical relationships occur in a cooling topic group because related alarms (warnings), such as cooling water tank conditions (cooling water tank level), cooling liquid flow and/or cooling liquid temperature are summarized in the topic cooling in this group.
In one embodiment of the system, the system has special rules as a pre-error system. These rules depend on whether a report (Precog) is generated for the (potential) impending error condition. For this purpose, for example, the report may be removed from the history of the log and/or extended by further findings.
In one embodiment of the analysis system, at least one of the following rules is used:
rule 1:
there are at least 1 alarm per group: group 1, group 2, group 3
Rule 2:
there are at least 1 alarm per group: group 1, group 2
Rule 3:
there are at least 1 alarm per group: group 2, group 3
Rule 4:
at least 3 alarms in at least one group: group 1, group 2, group 3
-rules 5;
at least 1 alarm in group 4.
In one embodiment of the evaluation system, which is designed to report an impending error situation, the evaluation system thus represents a pre-error system, the evaluation system determining messages for a specific point in time or a specific time period, wherein, in particular, only a warning for a specific time period (for example, a warning of the last 14 days, i.e., a warning message (example of a maximum value)) is considered in the calculation. Such time parameters can match the condition. This time period is in particular a characteristic time, which is defined during the development of the converter or the drive. This is particularly useful to ensure that the pre-error system only considers relevant warnings. The time stamp of the confirmed warning (confirmation time stamp) is not allowed to exist in the past in one design. The end of the activation phase of the time-stamped warning is confirmed. In one embodiment of the analysis system, the time stamp of the acknowledgement is taken into account in the analysis. In the analysis, it is advantageous to consider whether the warning message is confirmed without performing measures (e.g., repair, replacement, etc.), or whether the warning message is confirmed after performing an operation. For this purpose, a first example can be given as follows: within the last 24 hours, two important warnings occur successively. The first warning is part of group 1 and the second warning is part of group 2. When a second important warning occurs, the pre-error system is activated, as rule 2 (see above) can be applied here. A second example might yield the following: two important alarms occur continuously over the past 24 hours. Both of these warnings are part of group 1. When the second important message occurs, the pre-error system will not be activated because no rule can be applied here and then if another message of group 1 arrives, a message about the impending error will be generated because rule 4 can be applied (see above).
All warnings can be analyzed in real time by the analysis system without extensive technical knowledge during production, i.e. in particular during operation of the drive. Because continuous manual analysis is very time consuming, manual analysis is often not performed in a sufficient manner at the production facility. The method provides an automatic prediction of a potentially important situation before it occurs to prevent failure. Thus, potential downtime of the drives, for example within a production facility, can be reduced by scalability with respect to all drives connected to the analysis device. As a result, at least one of the following functions can be achieved, for example:
can notify of possible drive failures in the near future,
can inform why a drive failure can occur in the near future,
-suggestions and interpretations can be created to prevent failure of the drive.
In one embodiment of the analysis system, the cloud analysis method can be implemented using an analysis device, since log file data from the drive is stored in the cloud. Thus, a pre-error system is provided for extensible cloud instances in a suitable Python environment. These features enable process automation. The method is implemented by an algorithm based on expert knowledge.
In one design of the method, the driver's log is analyzed by expert knowledge (with appropriate labels) based algorithms, the log alerts are classified into different relevant groups and special decision rules are applied to the classification, which also takes into account the occurrence and identification/validation of these alerts.
Drawings
The features of the individual objects claimed or described can easily be combined with one another. Hereinafter, the present invention is exemplarily described and explained in more detail with reference to the accompanying drawings. Those skilled in the art can combine the features shown in the drawings to form a new embodiment without departing from the invention. Similar elements include the same reference numerals. Shown in the figure
Figure 1 shows an overview of the monitoring method,
figure 2 shows a time window of the report,
figure 3 shows a truth matrix,
figure 4 shows an analysis of the reaction time,
figure 5 shows the statistics of the data,
figure 6 shows a flow chart of a process,
figure 7 shows a first rule diagram,
FIG. 8 shows a second rule diagram, and
fig. 9 shows a third rule diagram.
Detailed Description
An overview of the monitoring method is shown according to fig. 1. The current transformer 1 is monitored. The current transformer 1 is part of a plant 6. The installation 6 also has a communication device 7 (DSC: drive system connection) which enables communication of the converter 1. Thus, a message such as a warning message (warning) 2 can be sent into a log file (german: logdatien). Such reports relate to errors or general events. Such reports are sent via the internet 4. To this end, for example, a workstation (DSEW: drive System expert workstation) can be provided. Thus, information about the health status (health level) 8 can be sent to the interface 5 for the analysis graph 9 of the drive. The log data (asset log) 10 is also sent to the analysis system 6, wherein the analysis system 6 has an analysis device 3. The analysis means 3 are for example provided with artificial intelligence. The analysis means 3 derives the health status of the installation 6, or of one of the components of the installation (e.g. the drive 1), and sends this health status 11 to the workstation 4 in the internet. The model is located in the analysis system 6. The model can be improved, i.e. updated, by means of a data analyst (data scientist) 12. The data analyst 12 is able to provide new errors 13 to the expert 13. The expert 13 can return an evaluated error 14 (marked as a fault). For example, when a future error (pre-failure) 15 has been derived, the expert 13 receives the mail 15. Expert 13 is also able to transmit information about potential errors 17 to user 16 via a User Interface (UI).
The diagram according to fig. 2 shows a time window 18 in which a state of health (health) 20 is plotted in relation to a time 19. There is a healthy state 21, a warning state 22, an error (pre-fault or predictive) state 23 to be identified, and an error state 24. The health status also decreases in this order. The time window 18 can be guided over these states, wherein a time line 25 for the warning is given as a function of these states.
The diagram according to fig. 3 shows a truth matrix (confusion matrix) 26. It shows how predictions comprising real events can be marked, i.e. identified.
The graph according to fig. 4 shows the derivation of the reaction time 27 until the error 24 occurs.
The graph according to fig. 5 shows the statistical prediction accuracy of the model for predictable errors. Here, the distribution of air-cooled and water-cooled model predictions is shown.
The diagram according to fig. 6 shows a flow chart similar to that of fig. 1. The data (asset log) of the converter 1 is led via a fleet management server 4 on behalf of the internet to an analysis system 6. The analysis system 6 performs the derivation of early detection of errors and can therefore also be referred to as a pre-error system. The analysis system 6 outputs a corresponding pre-error report (pre-fault) 30.
The diagrams according to fig. 7, 8 and 9 show different rule diagrams. Fig. 7 shows a first rule. Fig. 8 shows a second rule. Fig. 9 shows a third rule. Regions 31, 32 and 33 are formed for different events, respectively. Region 31 relates to groups 34, 35, 36 and 37 in which alerts are grouped. In the first group 34, alarms are grouped in relation to the input and the transformer. In the second group 35, messages, regulations and outputs relating to the batteries of the current transformer are grouped. In the third group 36, messages relating to cooling are grouped. In the fourth group 37, messages of warnings about bypasses of the power semiconductors in the converter are grouped. According to a first rule, at least one warning must be present in the groups 35, 35 and 36 in order to activate, i.e. trigger, the pre-error warning (Precog 1) 38. In this way, the error 41 can be prevented. As shown in the illustrations in fig. 7, 8 and 9, there are three different messages 38, 39 and 40 in the area 32 for alerting to impending errors (Precog 1, 2 and 3). In the region 33 for errors, there are three different errors 41, 42 and 43 to be predicted or to be prevented. The diagram according to fig. 8 shows a second rule according to which at least three warnings have to be present in the third group 36 in order to activate a pre-error report (Precog 2) 39 in order to be able to predict or prevent an error 42. The diagram according to fig. 9 shows a third rule according to which at least one warning has to be present in the fourth group 37 in order to activate the pre-error report (Precog 3) 40 in order to be able to predict or prevent errors 43.
Claims (11)
1. Method for monitoring a power converter (1), wherein a plurality of warning messages (2) are used for the purpose of ascertaining an error situation of the power converter (1), wherein the power converter has at least two types of warning messages, a first warning message type having warning messages which are dependent on the type of power converter and a second warning message type having warning messages which can be defined by a user, wherein a combined evaluation of a respective first type and a respective second type of warning is used for the purpose of monitoring, wherein the warning messages (2) of different types are grouped, wherein the grouping of warning messages (2) corresponds to an element of the power converter (1).
2. Method according to claim 1, wherein the warning message of the second type is dependent on the installation in which the converter (1) is integrated.
3. The method according to claim 1 or 2, wherein a warning message is marked, wherein the marking relates to a time sequence.
4. A method according to any one of claims 1 to 3, wherein the inference of an error involves a prediction of the error condition.
5. The method of any one of claims 1 to 4, wherein rules are used for monitoring, wherein the rules relate to the occurrence of one or more alerts in one or more groups.
6. The method of any one of claims 1 to 5, wherein artificial intelligence is used and/or trained.
7. The method according to any of claims 1 to 6, wherein a message is generated regarding the occurrence of the error condition in terms of time.
8. The method according to any of claims 1 to 7, wherein a message is generated regarding preventing the error condition.
9. Method according to any of claims 1 to 8, wherein a warning message is received by the analysis device (3) via the internet (4), wherein the analysis result is sent to the user interface (5).
10. The method according to any one of claims 1 to 9, wherein an analysis system (6) according to claim 11 is used.
11. An analysis system of a current transformer (1), in particular having artificial intelligence, wherein the analysis system (6) is spatially separated from the current transformer (1).
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP20201982.4A EP3985467A1 (en) | 2020-10-15 | 2020-10-15 | Monitoring of a power converter |
| EP20201982.4 | 2020-10-15 | ||
| PCT/EP2021/078618 WO2022079242A2 (en) | 2020-10-15 | 2021-10-15 | Monitoring of a converter |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116324656A true CN116324656A (en) | 2023-06-23 |
Family
ID=72915776
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202180070802.8A Pending CN116324656A (en) | 2020-10-15 | 2021-10-15 | Monitoring of a converter |
Country Status (4)
| Country | Link |
|---|---|
| US (1) | US20230305076A1 (en) |
| EP (2) | EP3985467A1 (en) |
| CN (1) | CN116324656A (en) |
| WO (1) | WO2022079242A2 (en) |
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| EP3985467A1 (en) * | 2020-10-15 | 2022-04-20 | Siemens Aktiengesellschaft | Monitoring of a power converter |
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| US6998962B2 (en) * | 2000-04-14 | 2006-02-14 | Current Technologies, Llc | Power line communication apparatus and method of using the same |
| AU2014201681B2 (en) * | 2009-08-18 | 2015-04-02 | Enphase Energy, Inc. | Method and system for distributed energy generator message aggregation |
| FR2963167B1 (en) * | 2010-07-20 | 2013-03-08 | Peugeot Citroen Automobiles Sa | DEVICE AND METHOD FOR COOLING A MEANS FOR STORING ELECTRIC ENERGY |
| DE102011087764A1 (en) * | 2011-12-05 | 2013-06-06 | Converteam Gmbh | Method for determining service life consumption of e.g. insulated gate bipolar transistor in wind energy plant, involves determining characteristics of temperature cycles, and determining service life consumption based on cycles |
| US20180045791A1 (en) * | 2015-03-16 | 2018-02-15 | Sikorsky Aircraft Corporation | Power supply condition monitor |
| US11243263B2 (en) * | 2015-06-04 | 2022-02-08 | Fischer Block, Inc. | Remaining-life and time-to-failure predictions of power assets |
| EP3327637B1 (en) * | 2016-11-25 | 2023-05-10 | Accenture Global Solutions Limited | On-demand fault reduction framework |
| JP6380628B1 (en) * | 2017-07-31 | 2018-08-29 | 株式会社安川電機 | Power conversion apparatus, server, and data generation method |
| EP3787165A1 (en) * | 2019-09-02 | 2021-03-03 | Siemens Aktiengesellschaft | Method for controlling the state of an electric device and assembly for carrying out such a method |
| EP3985467A1 (en) * | 2020-10-15 | 2022-04-20 | Siemens Aktiengesellschaft | Monitoring of a power converter |
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- 2020-10-15 EP EP20201982.4A patent/EP3985467A1/en not_active Withdrawn
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- 2021-10-15 EP EP21797966.5A patent/EP4185932B1/en active Active
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- 2021-10-15 WO PCT/EP2021/078618 patent/WO2022079242A2/en active Application Filing
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| WO2022079242A3 (en) | 2022-07-14 |
| EP4185932A2 (en) | 2023-05-31 |
| EP3985467A1 (en) | 2022-04-20 |
| WO2022079242A2 (en) | 2022-04-21 |
| EP4185932B1 (en) | 2024-07-03 |
| US20230305076A1 (en) | 2023-09-28 |
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